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    "<h1> PTRAIL Interpolation </h1>\n",
    "<h2> Linear and Cubic Interpolation Examples </h2>\n",
    "\n",
    "<p align='justify'>\n",
    "    This Notebook contains the examples for Linear and Cubic Interpolations.\n",
    "    It reads a file and interpolates points first using linear and then cubic\n",
    "    interpolation method by providing the dataframe and a time difference\n",
    "    threshold beyond which points need to be interpolated and the interpolation\n",
    "    type which by default is linear. The original data, linearly and cubicly\n",
    "    manipulated data are all plotted to show the difference between trajectories.\n",
    "    A segment of the original data is taken and interpolated and by plotting it\n",
    "    a clear difference in the interpolation points are shown.\n",
    "</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "The following datasets have been used in this Jupyter Notebook:\n",
    "<ul>\n",
    "   <li> <a href=\"https://github.com/YakshHaranwala/PTRAIL/blob/main/examples/data/gulls.csv\" target=\"_blank\"> Seagulls Dataset </a> </li>\n",
    "   <li> <a href=\"https://github.com/YakshHaranwala/PTRAIL/blob/main/examples/data/atlantic.csv%22%3E\"> Hurricane Dataset </a> </li>\n",
    "</ul>\n",
    "\n",
    "<hr>\n",
    "<p align='justify'>\n",
    "Note: Viewing this notebook in GitHub will not render JavaScript\n",
    "elements. Hence, for a better experience, click the link below \n",
    "to open the Jupyter notebook in NB viewer.\n",
    "\n",
    "<span> &#8618; </span>\n",
    "<a href=\"https://nbviewer.jupyter.org/github/YakshHaranwala/PTRAIL/blob/main/examples/4.%20Linear_And_Cubic_IP.ipynb\" target='_blank'> Click </a>\n",
    "</p>"
   ],
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    "pycharm": {
     "name": "#%% md\n"
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "from ptrail.core.TrajectoryDF import PTRAILDataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import folium\n",
    "from ptrail.preprocessing.interpolation import Interpolation as ip\n",
    "from ptrail.utilities.conversions import Conversions as con\n",
    "import ptrail.utilities.constants as const\n",
    "import pandas as pd\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 268 ms, sys: 16 ms, total: 284 ms\n",
      "Wall time: 283 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": "                               event-id  visible       lon       lat  \\\ntraj_id DateTime                                                       \n91732   2009-05-27 14:00:00  1082620685     True  24.58617  61.24783   \n        2009-05-27 20:00:00  1082620686     True  24.58217  61.23267   \n        2009-05-28 05:00:00  1082620687     True  24.53133  61.18833   \n        2009-05-28 08:00:00  1082620688     True  24.58200  61.23283   \n        2009-05-28 14:00:00  1082620689     True  24.58250  61.23267   \n\n                            sensor-type individual-taxon-canonical-name  \\\ntraj_id DateTime                                                          \n91732   2009-05-27 14:00:00         gps                    Larus fuscus   \n        2009-05-27 20:00:00         gps                    Larus fuscus   \n        2009-05-28 05:00:00         gps                    Larus fuscus   \n        2009-05-28 08:00:00         gps                    Larus fuscus   \n        2009-05-28 14:00:00         gps                    Larus fuscus   \n\n                            individual-local-identifier  \\\ntraj_id DateTime                                          \n91732   2009-05-27 14:00:00                      91732A   \n        2009-05-27 20:00:00                      91732A   \n        2009-05-28 05:00:00                      91732A   \n        2009-05-28 08:00:00                      91732A   \n        2009-05-28 14:00:00                      91732A   \n\n                                                                    study-name  \ntraj_id DateTime                                                                \n91732   2009-05-27 14:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-27 20:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 05:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 08:00:00  Navigation experiments in lesser black-backed ...  \n        2009-05-28 14:00:00  Navigation experiments in lesser black-backed ...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>event-id</th>\n      <th>visible</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>sensor-type</th>\n      <th>individual-taxon-canonical-name</th>\n      <th>individual-local-identifier</th>\n      <th>study-name</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">91732</th>\n      <th>2009-05-27 14:00:00</th>\n      <td>1082620685</td>\n      <td>True</td>\n      <td>24.58617</td>\n      <td>61.24783</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-27 20:00:00</th>\n      <td>1082620686</td>\n      <td>True</td>\n      <td>24.58217</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 05:00:00</th>\n      <td>1082620687</td>\n      <td>True</td>\n      <td>24.53133</td>\n      <td>61.18833</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 08:00:00</th>\n      <td>1082620688</td>\n      <td>True</td>\n      <td>24.58200</td>\n      <td>61.23283</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n    <tr>\n      <th>2009-05-28 14:00:00</th>\n      <td>1082620689</td>\n      <td>True</td>\n      <td>24.58250</td>\n      <td>61.23267</td>\n      <td>gps</td>\n      <td>Larus fuscus</td>\n      <td>91732A</td>\n      <td>Navigation experiments in lesser black-backed ...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "    First of all, lets import all the datasets one by one\n",
    "    and check out a few of their points and plot the\n",
    "    dataframe.\n",
    "\"\"\"\n",
    "\n",
    "# Reading the gulls dataset and converting to PTRAILDataFrame.\n",
    "# Also, lets, print the first 5 points of the dataset to\n",
    "# see how the dataframe looks.\n",
    "gulls = pd.read_csv('./data/gulls.csv')\n",
    "gulls_df = PTRAILDataFrame(gulls,\n",
    "                           latitude='location-lat',\n",
    "                           longitude='location-long',\n",
    "                           datetime='timestamp',\n",
    "                           traj_id='tag-local-identifier',\n",
    "                           rest_of_columns=[])\n",
    "gulls_df.head()"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     1501\n",
      "False     469\n",
      "Name: DateTime, dtype: int64\n",
      "CPU times: user 40.7 ms, sys: 33 µs, total: 40.7 ms\n",
      "Wall time: 38.4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# First, we will filter out a single trajectory from the sea-gulls\n",
    "# dataset and check how many of the trajectory's points have\n",
    "# time jump greater than what the examples are using to interpolate\n",
    "\n",
    "small_gulls = gulls_df.reset_index().loc[gulls_df.reset_index()[const.TRAJECTORY_ID] == '91732'][[const.TRAJECTORY_ID, const.DateTime, const.LAT, const.LONG]]\n",
    "time_del = small_gulls.reset_index()[const.DateTime].diff().dt.total_seconds()\n",
    "print((time_del > 3600*4).value_counts())"
   ],
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    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
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   "execution_count": 4,
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      "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, we plot the smaller trajectory on a folium map.\n",
    "sw = small_gulls[['lat', 'lon']].min().values.tolist()\n",
    "ne = small_gulls[['lat', 'lon']].max().values.tolist()\n",
    "coords = [zip(small_gulls[const.LAT], small_gulls[const.LONG])]\n",
    "m1 = folium.Map(location=[small_gulls[const.LAT].iloc[0], small_gulls[const.LONG].iloc[0]], zoom_start=1000)\n",
    "\n",
    "folium.PolyLine(coords,\n",
    "                color='blue',\n",
    "                weight=2,\n",
    "                opacity=0.7).add_to(m1)\n",
    "m1.fit_bounds([sw, ne])\n",
    "display(m1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 1970\n",
      "Interpolated DF Length: 3471\n",
      "CPU times: user 13.5 ms, sys: 20 ms, total: 33.5 ms\n",
      "Wall time: 3.95 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using linear interpolation.\n",
    "\n",
    "small_linear_gulls = ip.interpolate_position(small_gulls,\n",
    "                                            time_jump=3600*4,\n",
    "                                            ip_type='linear')\n",
    "print(f\"Original DF Length: {len(small_gulls)}\")\n",
    "print(f\"Interpolated DF Length: {len(small_linear_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 1970\n",
      "Interpolated DF Length: 3471\n",
      "CPU times: user 7.42 ms, sys: 20.1 ms, total: 27.5 ms\n",
      "Wall time: 3.82 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using cubic interpolation.\n",
    "small_cubic_gulls = ip.interpolate_position(small_gulls,\n",
    "                                           time_jump=3600*4,\n",
    "                                           ip_type='cubic')\n",
    "print(f\"Original DF Length: {len(small_gulls)}\")\n",
    "print(f\"Interpolated DF Length: {len(small_cubic_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x1800 with 3 Axes>",
      "image/png": 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1d+obb/vGlm13v3i3jl5/VDZjtfSuJU3unKyoStFb8DRyYETJvUkl3ISSe5MaOTByOsjIfCGjY7ljJfeTP5XX5c+5nMpk1OQ9l75HiUDXbtZXocqN8XvHlcuXqfjI5/TuL7675OMJANDaqu0EAQAA6ifQHIDGmA5JhySdK+kD1tp3brr9bkn/ZK2dKbDtdyU9KslK+qC19qYixxiWNCxJz3zmMy965JFHKvxWEESpue46E526/DmX63Pf+ZxO5E+Ecjzm0AMAoL3Ua15db8HTxH0Tmp6fPj2/2FB6SKMXj1YUngWdn+b3Ur+ngw8fLPp9vPM336nMFzOBjpnsTurw8OFQxo/2lT2S1c7bdkY9jC1zeCb3JgPNcyn5jx/m2waAeGIOQAAAolNsDsBAAeCGnTxR0p2SrrfW3r+27C8k9Um62hbYmTHmqdbaHxhjniy/bej11tovlzpOX1+fnZtjusB6CfIG2sHvHtTbP/123f/j+09v90vbfkm/OP4L5ZUvtmtJ/psC1lqd3X02b2wBABARb8HT+L3jmpmfOf33fjA9qLFLxur+dzmssK5eY0vvS2slt1J0nW2d22RkdOxk6eq+oDYHJkC1vAVPu27fpX/70b8VvP38J5+v5zzxOfr00U+XnYepWsnupBb3LJ7+OuEmZBX8nLKQHqdH89fN1/z8ENbzXpTPnwDQqoK8xgrr+R4AAJwplABwbUcZSY9Za//WGPMmSddJutxaW/wv/OPbvlvSsrX2b0utRwDYnIK8mFvnJBztOm+X3nPpe3hhBwBAg9WrCi8uyv18Ln3Wpfrsdz4bWoCyOTAB6qmS1+yVKlS5UUkFYCX7rVRYz3s8fwJA9XgOBQAgGsUCwLITSRhjzlmr/JMx5ixJvyvpW8aYKyS9U9JVxcI/Y8wTjDFnr/9f0isk3V9oXTS/1PaUZnfNFpzDZrPcqZxuu/82Pf8Dz1f2SLZBIwQAAN6Cp4H9A1rJrWwJsHKnclrJrWhg/0Bbz7tVbL7B4YuGNX/dvL78vS+HFv45CUdD6aFQ9gUEUclr9koVmsNzMD1Y83EKzS24rtxcnOvrhPG8V8l+gowLANpNuddYhH8AADRW2QpAY0xa0i2SOuQHhrdba99jjDkqqVvSz9ZWvc9ae50x5qmSpqy1O40xvya/ZagkdUq6zVr7V+UGRQVgc1tv63XL12/R8upy2fW7El365u5vUgkIIBDabgG1Yf6V2oXR0nAdra4Qlc2teHucHj22+ljV9+2uji594ppPbHnzNqyKw82tcr0FT3901x/pi498ccu6nYlOdXV0na4kCet5L6w5QqlwAQAAANBIobUAbQQCwNYwcmBE++b2BXoTYfCCQU1fXfiqXgBYR8sYoHZB2/HRlrK4MFoarrvnjffwvIWmkT2S1as+9qqKK1yfu/25uufae4oG2aX+fgc91sbnpOyRrF79sVdr9dRqyW3WA/YXffBFoTzvhfXYJ/gHAAAA0EhVtwAFipmZnwl8BfHHHvhYnUcDoNXRthAIR5Dq/ErWa0dhtDSU/AugCP/QTPp39OvB3Q9q8ILBQOtv37Zd06+Z1rev/3bJMKtUy7drz7+27ONpY6tcb8HT1bdfXTb8k6TV/Kom7psI7XkvrOfFXD6nifsmQtkXAAAAAFSLCkBUrdL2WDbTfPe1ZkcrRLQT2hYC4aACsHZhtTQ8ev1R/l6j7QV5PG2smKuky4jkP5dZa5uqAjDIsQAAAAAgLFQAInS9Xb1RDyHWskeySu9La+rwlJZWl2Tlv7ExdXhK6X1pZY9kox4iEKqZ+ZmybcJyp3KanqedMFBKkOq1jdU22Cq1PaXZXbPqcXpqqgQk/ANKP56chKMep0ezu2ZPP14q6TIi+VV7YT3vhVX9uz6uILwFTyMHRpTcm1TCTSi5N6mRAyN0PAAAAABQMwJAVG0wHax1ECpHK0S0I9oWAuEYu2RMTkeZN8I7HI1ePNqgEbWmQi0NwwoGgHZTqkXo/HXzZ7TKrfTvfG9Xb2jPe0H2U8m4yuGCPzQbAmkAAIB4IQBE1cYuGQu8bofpqONI4mf83nHl8mUqoZhbBDETtKqY6mOgtEqrbVBcantKkzsntbhnUfkb8lr9v8vPSQagsM2Pp8U9i5rcObnluajSv/ND6aHQnveC7Gfnjp2hVBsGveDv4HcPEsigIQikAQAA4ocAEFVLbU/pZc96WaB1rbWcMFSAVohoR7QtBMJTSbUNKnPVjquCrffcYOsBOFOlXUbWq/rCet4rt5/3XfG+UKoNg1zwd+LkCb1i+hUEMqg7OtAAAADEk7E2+PwKjdLX12fn5uaiHgYC8BY8nfeB87R6qvwV8T1Oj+avm6fiIICEmwg090nCJJS/Id+AEQH15y14Su9LayW3UnQdnkcARM1b8PS8DzxPJ0+dLLpOZ6JT39r9LZ6rgCp4C57Off+5gdZNKKF8pvGvhbNHshrYP6BcPndGWOIkHDkdjmZ3zZYNHJN7k1paXappHLwuQlhGDoxo6vBUyYtQnYSj4YuGNblzsoEjAwAAQBDGmEPW2r7Ny6kARE1S21P6xOs/EajFJy0rg6MVIuKiknlEaFsIoBWktqd01+vvUndHd8Hbuzu6ddfr7+K5CqhSJV1GTulUJC0xw6g2DGNOY86vUEylc/nRgQYAACCeqABEKHr/uleP5R4ru16yO6nFPYsNGFFr4wpMxEG1V8d7C54m7pvQ9Py0lleX1dvVq6H0kEYvHuUNdQBNg+cqoH68BU/P/8DzywYS6yqpvGsWYVQASpxfYatqXoPTgQb14C14Gr93XDPzM6dfKw2mBzV2yRivlQAACFmxCkACQISCE4Zw0QoRrY77MAAAqFb2SFav+afX6ET+REXbtdJriyAX/AXB+RU2qvY1eNBAmsAZQYXRKhkAAARHC1DUFS0rw0UrRLS68XvHlcuXaSNE2yoAALCJt+BpYP9AxeGf1FqvLcYuGZPT4ZRfsYxqzq8qbQ+J1lHta/DB9OCW887NnISjofRQzWNE/K0/j6/kVrZc5JA7ldNKbkUD+wd4zgEAoAEIABEKThjCF8bcIkBUmEcEAABUI0iAUUwrvbYod8FfZ6Kz7Dzr1ZxfZY9kld6X1tThKS2tLsnKaml1SVOHp5Tel1b2SLbi7wXNo9rX4EECaafD0ejFozWPEfHHxaAAADQPWoAiFLT7A7ARbYEBAEA1ap0br9VeWxSbT/Tq51+tV370laGeX3HOFn+1vAanZSPCQktZAAAajxagqCtaVgLYiLbAAACgGsuryzVt32qvLVLbU5rcOanFPYvK35DX4p5FTe6c1GXPuSz08yuqcuKvltfgdKBBWII+j9f6fA8AAMojAERoOGEAsI62wAAAoBq1BHhxe20R9vkVLdrjr9bX4MUCaS7kRSW4GBQAgOZBC1AAQOhoMQUAAKoxcmBEU4enygZVhfDaojRatMcfr8HRDII8jzsJR8MXDWty52QDRwYAQHzRAhQA0DC0BQYAANUYu2RMTkfpCqbNeG0RTNyrcrwFTyMHRpTcm1TCTSi5N6mRAyPyFryoh9YwvAZHMwjyPO50OBq9eLRBIwIAoH0RAAIA6oK2wADQ3ngzHtUoF2Bs69ymK3dcyWuLKtS7RXuUj/nskazS+9KaOjylpdUlWVktrS5p6vCU0vvSyh7J1n0MzYLX4IgaQTQAAM2DFqAAAAAAQpU9ktXA/gHl8rkzWoA5CUdOh6PZXbO8CY2SvAVPE/dNaHp+Wsury+rt6tVQekijF4/ypnGV6tkeMsrHPG0vgebE8zgAAI1TrAUoASAAAACA0PBmPNC86hHURf2Yj3K+MW/B0/i945qZnzkdcAymBzV2yRjPbwAAAGgY5gAEAAAAUHfj944rly/+Rrwk5fI5Tdw30aARAVhXj/aQUT/mZ+ZnSoZ/kpQ7ldP0/HSox6XtKAAAAJodFYAAAAAAQpPcm9TS6lL59bqTWtyz2IARIU6ouGo+UT/mE25CVuXf10iYhPI35EM5ZtRVjwAAAMBGVAACQJvzFjyNHBhRcm9SCTeh5N6kRg6MyFvwoh4aACBGlleXQ10PWEfFVXOK+jHf29Ub6npBRF31CLQqzkkBAGgsAkAATYETgfriDTMAQKNE8WY84s9b8DSwf0AruZUt7R5zp3Jaya1oYP8Arx0jEPVjfjA9KCfhlFzHSTgaSg+Fdsyo2o4CrYxzUgAAGo8AEJEi9IHEiUC98YYZAKCRongzHvFHxVVz2XgeF6T9Zz0f82OXjMnpKPOc0+Fo9OLR0I4ZddVjK9v8HkDvX/fq/H84X71/3ct7AjHGOSkAANEgAERkCH0gcSLQCLxhBgBopCjejEf8UXHVPDafxwVRz8d8antKs7tm1eP0bLn4wEk46nF6NLtrNtS5+KKuemxVhd4DeCz3mB74yQN6LPcY7wnEGOekAABEgwAQkSD0wTpOBCpXaeUsb5gBABopijfjEX9UXDWHUudxhTTqMd+/o1/z181r+KJhJbuTSpiEkt1JDV80rPnr5tW/oz/U44Vd6dwOnXEque/wnkD8cE4KAEA0CAARCUIfrGuVE4FmOSmvpnKWN8wAAI3W6DfjEX9UXDWHIOdx6xr9mE9tT2ly56QW9ywqf0Nei3sWNblzsi7BY5BK585Epx499mjZ84d26YxTyX1nXSXvCTTL+RoK45wUAIBoGGtt1GPYoq+vz87NzUU9DNRR0Lkikt1JLe5ZbMCIEJWEm5BV+eehhEkof0O+ASPaKnskq4H9A8rlc2eElU7CkdPhaHbXbEPe1PAWPKX3pbWSWym6To/To/nr5s94o4PHGwAAaHWDdwzq1vtvLbvetRdcq5mrZxowovryFjyN3zuumfkZLa8uq7erV4PpQY1dMhZp9SyvKx9X6hwhYfxrrU/ZUyXPH6p9fd+Kgt53tmwX4L7ULOdrKI7nDgAA6ssYc8ha27d5ORWAiARXf2Fds1/N3UztaqutnA27RREAAECjBblgTJKMTJ1HUn/NXBHWbOdxhaq+rr3jWg3eMVj3SrBilc6ve8HrZIzRifyJsucP7dQZp9r7RLntmul8DcVxTgoAQDQIABGJZg990DjNfiLQTCfl1bZLDdKiyOlwNHrxaM1jBAAAqIe7H7o70Hp3PXRXnUdSX80eZjTTeVyxoPS2+2/Trfff2pDwtFDb0WR3UvlTpTuXrJ8/tMp0CGGo9j5RbrtmOl9DcZyTAgAQDQJARKLZQx80TrOfCDTTSXm1V1yntqc0u2tWPU7Plsedk3DU4/Rodtdsy7cVAgAA8dVslWf10uxhRrOcx5UKSgupNjytZl65Ss4f2uV+LQW772wW5L7UTOdrKI5zUgAAokEAiEg0e+iDxmn2E4FmOimv5YrrYi2Khi8a1vx188yJAQAAmlozVZ7VU7OHGc1yHhckKC2kkvC02laslZw/tMv9Wgp239ksyH2pmc7XUBrnpAAANB4BICLR7KEPGquZTwSa6aS81iuuC7Uomtw5yeMMAAA0vcH0oDpMR8l1OkxHy3cQafYwo1nO44IEpYUEDU9racVayflDs1RUNkKp+85mldyXmul8DeVxTgoAQGMRACIyzRz6oPGa9USgmU7Km+WKawAAgEYbOG9AeVt6XrW8zevq51/doBHVRyuEGc1wHldLABpk21pasVZy/tBur+8L3Xd6u3p1/pPPV29Xb1X3pWY6XwMAAGg2xlob9Ri26Ovrs3Nzc1EPAy3CW/A0fu+4ZuZnTrdRGUwPauySscjDI7Q+b8FTel9aK7mVouv0OD2av26+Ife37JGsBvYPKJfPnXE1spNw5HQ4mt01S3gOAABiZ+TAiG46dFPJELDDdOi6vus0uXOygSM7U63nJiMHRjR1eKpkdZuTcDR80XCk32fUknuTWlpdqm7b7qQW9yyGsv9C+6r0/IHX97VptvM1AACAKBhjDllr+zYvpwIQLa3aeRmAoJqlzdG6ZrjiGoiSt+Bp5MCIknuTSrgJJfcmNXJgpGALLgBAfMzMzwSqAIxqbjwpnHOTdqsIq1aQqq9CglaC1dKKtdLzB17f16bZztdQO17vAwAQHioA0bK40g+N5C14mrhvQtPz06ev5h5KD2n04lHuX0CDcIU8ALSvhJuQVflz14RJKH9D6aCwHsI8N+HvXXlBft6FBP0d1FIBuHGMnD80Dj/veOD5DwCA6hSrACQARMuiPQ4AtA8u+gCA9lZtINOo6QLCPjchzCivWFBQSKXhAeeaQOPxeh8AgOrRAhSxMzM/U/ZEL3cqF2kbIABAOMbvHVcuX+Y5P5/TxH0TDRoRAKCRgrR83NzesZHTBYR9bpLantLkzkkt7llU/oa8FvcsanLnJG96b1CsdebgBYO69oJra2qnSSvW+qC1I0rh9T4AAOGjAhAtq55tgBp1pTAAIJgwWnEBAFpXpZUhja4kafYWpagcrQjDxc8T5fB6HwCA6lEBiNjp7eoNdb11jbxSGAAQzPLqcqjrAQBaw3rF0Is++CIdyx2TJBmZM9ZxEo56nB7N7po9HebVo5KkVPVSvc5NEJ1iFYaVVhPCf+wM7B/QSm5lS6Vs7lROK7kVDewfoBKwDW18Xg0S/km83gcAoBJUAKJl1WNeBnrOA0Bz4opgAGg/6xVDq/lVnTx1suA6ye5kwbnxwv67Ua566dJnXarPfuezzBkHFMCciiikknk8N+L1PgAAW1EBiNipx7wMYV4pzPwGABCeauZ+AoBq8BquOWysGCoW/knSxCsmCs6NF2bleJDqpc8//Hl1JjpL7oc549DM6vncF/YcmWh9pZ5XS+H1PgAAlSEARMtKbU9pdtesepyeLW8KF2oDFERYJya0EQWAcNXjog8A2IzXcM1j/N5xreZXy643/KnhggFFmC05g1wkmD+V1+XPuTzUcxOgUer93Ecrd2wW5Hm1EF7vAwBQGQJAtLSw52UI48SE+Q0AIHz1uOgDADZqxddwca5WnJmfKVn5ty5v8wW7c4RZOR70IsEvf+/LzBmHltOI5z7myMRmQZ5XN+L1PgAA1SEARMtLbU9pcuekFvcsKn9DXot7Fgu2AQoijBOTMNuIAgAeF/ZFHwCwUau9hot7tWIllUCFunOEWTleyUWCYZ6bAI3QiOc+Wrljs0qe43m9DwBA9QgAgQ3CODFhfgMAqB/eWAVQL630Gq4VqxUr4S14ZefT26jQG8lhVo5TvYQ4q/a5r5IKZFq5Y7Ogz5fJ7iSv9wEAqAEBILBBGCcmzG8AAADQelrpNVyrVStWYr2yMUj7z3XF3kgOq3Kc6iXEWTXPfZVWIMexlXucWzA3As+rAAA0hrHWRj2GLfr6+uzc3FzUw0Cbyh7JamD/gHL53BlXQjoJR06Ho9ldsyXfLEjuTWppdanscdavZJP8k4fxe8c1Mz+j5dVl9Xb1ajA9qLFLxlrqJAgAAKBVVfMaLiqtNNZKeAue0vvSWsmtBN6m03TqrX1v1eTOyUjH1eP0aP66eV67o+VU+nxSy+PBW/A0cd+EpuenT5/3DqWHNHrxaEs9dmp9zwA8rwIAEDZjzCFrbd/m5VQAApvUeqVwpVeyxX3+FgAAgFbQStUI9apWjLqiZfzeca2eXK1om67Orrq3DYxj9RKwrtLnvloqkOPQyj3uLZgbhedVAAAagwpAIGSVXMkmiaveAAAAmkArVSPUowKwGSpaev6qR8dOHgu0bqfpVFdnV0MrbeJSvQRsVOlzX1wrkIMaOTCiqcNTJedNdBKOhi8armtlclzwvAoAQDiKVQASAAJ1EPQNFE4eAAAAmkczhGBBhP0ashnCT2/B07nvPzfw+rtfvJs3iIGQVPLcl3ATsir/PlLCJJS/IV+3MUel3QNQAADQnGgBCjRQ0DaiM/MzJd+4kfw2ItPz040YNgAAQFurtRV8o4xdMiano0zLvg4ncGvMWlr6heWGL9wQeN1kdzJQ28CoW5qGJS7fB5pXJc99vV29gfYZdL1WU68WzAAAAPVABSAQoXa/ehIAAADVCbNaMeqKlpsP36w/vPsPA6+/+8W7y1Y2tko1Zzlx+T4QH+3exSbq50sAAIBCqq4ANMZsM8Z8xRjzdWPMA8YYd235dmPM54wxR9Y+/1KR7a8wxnzbGHPUGLOn9m8FiI92v3oSAAAA1QmzWjHKipb3/st7Kwr/JJWtbPQWPA3sH9BKbmVLSJE7ldNKbkUD+weavoIuLt8H4iXsCuRWM5gelJMo8/0nHA2lhxo0IgAAgOKCtAA9Iekya+0LJV0o6QpjzMWS9kj6vLV2h6TPr319BmNMh6QPSOqXdJ6kNxhjzgtp7EDL4+QBAAAA1UptT2ly56QW9ywqf0Nei3sWA7XG3Cyqi9JuPnyz3nXwXRVt0+P0lP3+mqGlaRji8n0gXlLbU5rdNasep2fLuayTcNTj9Gh212xs5+ds9wB0Ha2JAQBoDWUDQOtbv9TTWfuwkl4l6Za15bdIenWBzV8i6ai19jvW2lVJH1vbDoA4eagWJxsAAADhieKiNG/Bq7jyz8jozRe++fT2xV4PxmWe7bh8H4ifVpkvtR7aPQCV/NbE6X1pTR2e0tLqkqysllaXNHV4Sul9aWWPZKMeIgAAWBNoDsC1Sr5Dks6V9AFr7TuNMT+31j5xwzqPWmt/adN2A5KusNb+0drXQ5L+q7X2TwocY1jSsCQ985nPvOiRRx6p/rsCWkiU83p4C57G7x3XzPyMlleX1dvVq8H0oMYuGSt7wlLLtrVgHhQAAIBweQue0vvSWsmtFF2nx+nR/HXzob3O63A7dEqnKt7u6PVH9dDPHir5evBY7lgs5tlmvnCgeXkLnibum9D0/PTp8+Gh9JBGLx6NdfgXxd8LAABQXtVzAEqStTZvrb1Q0tMlvcQYc37Q4xbaXZFj3GSt7bPW9p1zzjkBdw+0vqiunqzlqr2orvhjHhQAAIDwNbqi5crbrqwq/JOkRxYfKft6MEhoJjX/PNvMFw40r7BaMLcaWhMDANBaAgWA66y1P5f0RUlXSPqRMeYpkrT2+ccFNvm+pGds+Prpkn5QzUCBOGv0yUMtQVqUIRwnGwAAAPXRyIvSDhw5UPW2b8++vezrQbP2r5RWmGeb+cIBNBtaEwMA0FrKBoDGmHOMMU9c+/9Zkn5X0rck3SXpTWurvUnSJwts/lVJO4wxzzHGdEl6/dp2ACJUS5AWZQjHyQYAAED9NOKitJsP31zT9vf/5P6yrwft2r9SWmGebeYLB9BslleXQ10PAADUV5AKwKdI+oIxZl5+oPc5a+2nJL1X0suNMUckvXztaxljnmqMuUeSrLUnJf2JpM9IelDS7dbaB8L/NgBUopYgLcoQjpMNAACA1uUteBr+1HBDjmVkGtbStF4a3ZoVQLS8BU8jB0aU3JtUwk0ouTepkQMjTTXFBa2JAQBoLWUDQGvtvLX2RdbatLX2fGvte9aW/8xae7m1dsfa54W15T+w1u7csP091trnWmtT1tq/qt+3AiCoWoK0KEM4TjYAAABa1/i948rbfEOOdXb32ZHMsx22qOYLB9BY2SNZpfelNXV4SkurS7KyWlpd0tThKaX3pZU9ko16iJJoTQwAQKsx1gabIL2R+vr67NzcXNTDAGIruTeppdWl8ut1J7W4ZzG0bWs1cmBEU4enSlYgOglHwxcNa3LnZKjHBgAAQG2Cvo4sxkk4+vVf/nV9+2ff5vUggNjwFjyl96W1klspuk6P06P56+YbUvHrLXgav3dcM/MzWl5dVm9XrwbTgxq7ZEySmmqsAADAZ4w5ZK3t27w8SAtQADFTy1V7UV7x16h5UFqh9QoAAECrqbVDhNPh6O/7/5558QDEyvi948rly0yzkc9p4r6Juo+lXCXiQz97iNbEAAC0EAJAoA3VEqQ1KoQrpBHzoLRK6xUAAIBWU2ub9tlds7rsOZfx5jOAWJmZnylZ1SxJuVM5Tc9P13Uc3oKngf0DWsmtbBlP7lROK7kVDewf0HN/+bm0JgYAoEUQAAJtqJYgrREhXCn1nAcl6AkPlYAAAACVG0wPVr/tBYOnX+cxLx6AOAlaHV1rFXU5lVQipranNLlzUot7FpW/Ia/FPYua3DnJxRcAADQZ5gAE2pi34GnivglNz0+f7u0/lB7S6MWjZV+417Jts2KOQQAAgPrxFjydf+P5On7yeEXbMZ8UgDgLOj9qsjupxT2LsR8HAACoXLE5AAkAAWANJzwAAAD1lT2S1Wv+6TU6kT9Rdt0O06Huzm7N7pqlqq8BvAVP4/eOa2Z+5vQFfoPpQY1dMkb4CtRRs1yImnATsir/HmHCJJS/IV+3cQAAgMoVCwBpAQoAa5ql9QoAAEBc9e/o1wMjD2jwgsEt7eQ36u3q1XV919HSs0GYBxuIztglY3I6ij8fSpLT4Wj04tG6jiPoPK21zucKAAAapzPqAQBAs+jt6g1UAcgJDwAAQPVS21Oavnpa01dPRz0U6Mx5sDfLncopdyqngf0DtGFFbEVd/ZrantLsrlkN7B9QLp87oxLQSThyOhzN7pqt+1gG04OBKhGH0kN1HQcAAAgPFYAAsGYwXfpKdIkTHgAAAMTL+L3jyuWLv+EvSbl8ThP3TTRoREDjNEv1a/+Ofs1fN6/hi4aV7E4qYRJKdic1fNFw1ZXQ3oKnkQMjSu5NKuEmlNyb1MiBEXkLXsH1m6USEQAAhIc5AAFgjbfgKb0vXfDq53U9Tg9XPwMAACA2mAcb7SrO53/ZI9myFYWFQsVqtwMAANFiDkAAKGO99UqP07OlEtBJOOpxehrSegUAAABoFObBRruKa/Xrxra+m9t55k7ltJJb0cD+gYKVgPWoRAQAANGhAhAANvEWPE3cN6Hp+enTc0AMpYc0evEo4R8AAABihQpAtKu43vdHDowEmstv+KJhTe6cbODIAABAvVABCAABpbanNLlzUot7FpW/Ia/FPYua3DnZsPCv0rkaAAAAgGoxDzbaVVyrX2fmZ0qGf5JfCTg9P92gEQEAgKgQAAJAE2mWSegBAADQHsYuGZPTUSYA7HA0evFog0YENEZvV2+o6zWLuAabAACgcgSAANAkapmrAQAAAKgG82CjXcW1+jWuwSYAAKgcASAANIm4TkIPAACA5ta/o1/z181r+KJhJbuTSpiEkt1JDV80rPnr5tW/oz/qIQKhi2v1a1yDTQAAUDljrY16DFv09fXZubm5qIcBAA0V10noAQAAAKAZZY9kNbB/QLl87owuLE7CkdPhaHbXbMsF4N6Cp/S+tFZyK0XX6XF6NH/dPJW9AADEhDHmkLW2b/NyKgABoEkwVwMAAABalbfgaeTAiJJ7k0q4CSX3JjVyYIT29Whqcax+pa0vAABYRwUgADQJKgABAADQiuJYRQW0Om/B08R9E5qen9by6rJ6u3o1lB7S6MWjhH8AAMRMsQpAAkAAaBIjB0Y0dXjqjDdNNnMSjoYvGtbkzskGjgwAAAAojHaDAAAAQLRoAQoATS6uk9ADAAAgvsbvHVcuX/wCNknK5XOauG+iQSMCAAAAIBEAAkDTYK4GAAAAtJqZ+ZmSHSwkKXcqp+n56QaNCAAAAIBEAAgATSWOk9C3O2/B08iBESX3JpVwE0ruTWrkwIi8BS/qoQEAANRseXU51PUAAAAAhIM5AAEgxrwFT+P3jmtmfub0xO+D6UGNXTJGJWEDZI9kNbB/QLl87owr452EI6fD0eyuWUJdAADQ0pJ7k1paXSq/XndSi3sWGzAiAAAAoL0wByAAtJnskazS+9KaOjylpdUlWVktrS5p6vCU0vvSyh7JRj3EWPMWPA3sH9BKbmVLW6zcqZxWcisa2D9AJSAAAGhpg+nBLe3rN3MSjobSQw0aEQAAAACJABAAYonwKXrj944rly8zH04+p4n7Jho0IgAAgPCNXTImp6NMANjhaPTi0QaNCM2k3drht9v3CwAAmhstQAEghkYOjGjq8NSW8G8jJ+Fo+KJhTe6cbODI2gftsAAAQLug7TkKKXe/eP8V79fcD+diM10BjwMAABCVYi1ACQABIIYIn6KXcBOyKv83NmESyt+Qb8CIAAAA6sdb8DRx34Sm56dPhzlD6SGNXjzakmEOauMteErvS2slt1JyPSNzxmvmVg3Lgny/PU6P5q+blyTmaQcAAKEiAASANkL4FD1CWAAAALSrkQMjuunQTcrb6s411sOyVgnEgnZg+b3U7+ngwwepEgQAAKEqFgAyByAAxFBvV2+o66Fyg+lBOYky8+EkHA2lhxo0IgAAAKAxPvL1j1Qd/kmtN1f2zPxMyfBP8udi/9SRTzFPOwAAaBgCQACIIcKn6I1dMiano8zvoMPR6MWjDRoRAAAA0BiP5R6rafvcqZym56dDGk39La8uh7KfVgs+AQBAcyMABIAYInyKXmp7SrO7ZtXj9GwJY52Eox6nR7O7ZlumrREAAADQSGGFao0QVmeVVgs+AQBAcyMABIAYInwqzlvwNHJgRMm9SSXchJJ7kxo5MFKXVjv9O/o1f928hi8aVrI7qYRJKNmd1PBFw5q/bp75PQAAAIAiTtlTdX2tHqYgHViCaqXgEwAANDdjrY16DFv09fXZubm5qIcBAC3PW/A0cd+Epuentby6rN6uXg2lhzR68ahS21PyFjyN3zuumfmZ07cPpgc1dslYLMPB7JGsBvYPKJfPnTHvhpNw5HQ4mt01SygHAAAA1Kj3r3trbgO6rhVeq3sLntL70lrJrdS8r2R3Uot7FkMYVeO12/klAADNwhhzyFrbt2U5ASAAtKd2C8OCnJT3OD2av26ek1MAAACgBiMHRnTToZuUt/nQ9tnsr9XLnV9d+qxL9dnvfPaM2zZzEo6GLxrW5M7JRgw5VO12fgkAQDMpFgDSAhQA2pC34Glg/4BWcitbTkBzp3Jaya1oYP9A07faqcT4vePK5YufbEtSLp/TxH0TDRoRAAAAEE9jl4ypu7M71H1ufK3eyLb+QZVr///3/X8f23na2/H8EgCAVkAACABtqB3DsJn5mZJX20r+yen0/HSDRgQAAADE16XPurTsOkYm8P7WX6tnj2SV3pfW1OEpLa0uycpqaXVJU4enlN6XVvZItpZh1yS1PaXJnZNa3LOo/A15Le5Z1OTOSaW2p2I9T3s7nl8CANAKCAABoA21Yxi2vLoc6noA8P9v7+6jI7vqM98/+5SO1C7KNYwIJIGQmCnkADbFSwum+5IQMHMTV/MWTMngIEECQyHkEKMo66aZe5dNkwU9uTeKIMhBIcIBJCBGZZjgNEVg2SGTzO0mqDWmsOME+QSYMDCxJ3VHKVHurqOqff9odUfv9aJT79+PV6+WjvbZ56dyn+5SPfXbGwAA7HY5oPvy3395z6/3mT4ZGUUHopp40YQeedcjVQeB+Yv5ju40q9Ql2KlLZPbiz5cAAHSCvlYXAABovl4MwyL9EeWL+arGAQAAAKjd1qUg99Pf179rL79qn6v3OX1Vd5rVs4+el/M0fXZai9lFrRfXFemPaDQ+qqnjU4F15l3uEuzEff7204s/XwIA0AnoAASAHlRtyNVNYdhofHTXUjs7uY6rsfhYkyoCAAAAuku9S0FW+1xdUlWdZnd+/c6a9wZs56VF210v/nwJAEAnIAAEgB7Ui2HY1PEpuaEK33PI1eSxySZVBAAAAHSXepeCrPa5eqW5t6olwNvaudiJS4u2Wi/+fAkAQCcgAASAHtSLYVhsMKb0SFphN7zrh1PXcRV2w0qPpANb2gcAAADoNfUuBVntc/Wr+q6quaZqArx6OxdxSS/+fAkAQCcgAASAHtSrYVhiKKHseFapoylFB6JyjKPoQFSpoyllx7NKDCVaXSIAAABq4OU8TZyZUPR0tOYlHxG8wywFWem5+rVPuramDsCdDgrw6u1cxCW9+vMlAHQSnjP1JmOtbXUNuwwPD9vl5eVWlwEAXc/LeZo5N6OF7MKVTe7H4mOaPDbJD2cAAABoa5nVjJJLSfklf1t44zqu3JCr9EiaN3g12cSZCc2vzB8YprmOq9TRlGZPzNY89x+u/KE2yht11xcdiGrt5Nqu484pR1aVXx9zjKPS7aW6r9/t+PkSANoTz5m6nzHmvLV2eNdxAkAAQCt4OU/TZ6e1mF288sPhaHxUU8en+OEQAAAAB/JynuJzcRX8wr5jwm5Y2fEszy2bqJH/X6Kno8oX84eqb78Ar9q59wsQAQBoVzxn6g37BYAsAQoAaLrMakbxubjmV+aVL+ZlZZUv5jW/Mq/4XFyZ1UyrSwQAAEAbY8+29tTIpSCr3V/wIGVbljllZE4Z9f9Wv950z5vk5TyNxkd31buT67gai48dugYAAJqJ50y9jQAQABCIatcS93KekktJFfzCrqWB/LKvgl9QcinJGuQAAADYF3u2ta9G7btd7f6C1fLLvj794Kd13e9fp+EfH5YbqhAAhlxNHpsMtAYAABqN50y9jQAQAHBotXT08c4jAAAAHFa13WBBdI2hdrHBmGZPzGrt5JpKt5e0dnJNsydmD7W0WDVdevW4WLqoWzO36sM3frghnYsAALQSz5l6GwEgAOBQau3o451HAAAAOKxqu8GC7hpD60wdn6rYpSdJfaav5rkvblzUyv9YaUjnYitUuzoLAKD78ZyptxEAAgAOpdaOPt55BAAAgMNiz7beU83+gh979cf0juF3XAnwqmVltZBdaEjnYrOx3zoAYCueM/U2AkAAwKHU2tHHO48AAABwWNV0g7FnW/eptL/gW1/41m0BXi264Q2I7LcOANiJ50y9jQAQAHAotXb08c4jAAAAHFY13WDs2dadGtWl1w1vQGS/dQDATjxn6m0EgACAQ6m1o493HgEAACAIlbrBEkMJ9kI7hF577LrhDYjstw4AkHb/G/6G9Bv0iz/9i7r5ups7fp9b1MZYa1tdwy7Dw8N2eXm51WUAAKowcWZC8yvzB/6g6TquUkdTmj0xK+nSvhTJpaT8kr/tPNdx5YZcpUfSPPkAAADAofCcs37d8tiZU6bqsY+865GO735wTjmyqvw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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, plot the scatter points of following 3 trajectories:\n",
    "# 1. Original Small DF.\n",
    "# 2. Linear-Interpolated Small DF.\n",
    "# 3. Cubic-Interpolated Small DF.\n",
    "\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(25, 25))\n",
    "axes[0].scatter(small_gulls[const.LAT],\n",
    "                small_gulls[const.LONG],\n",
    "                s=100, color='brown')\n",
    "axes[0].set_title('Original', fontsize=60, color='grey')\n",
    "axes[1].scatter(small_linear_gulls[const.LAT],\n",
    "                small_linear_gulls[const.LONG],\n",
    "                s=100, color='blue')\n",
    "axes[1].set_title('Linear', fontsize=60, color='grey')\n",
    "axes[2].scatter(small_cubic_gulls[const.LAT],\n",
    "                small_cubic_gulls[const.LONG],\n",
    "                s=100, color='green')\n",
    "axes[2].set_title('Cubic', fontsize=60, color='grey')\n",
    "\n",
    "fig.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.93 s, sys: 115 ms, total: 8.04 s\n",
      "Wall time: 8.04 s\n"
     ]
    },
    {
     "data": {
      "text/plain": "                                             Name      Date  Time Event  \\\ntraj_id  DateTime                                                         \nAL011851 1851-06-25 00:00:00              UNNAMED  18510625     0         \n         1851-06-25 06:00:00              UNNAMED  18510625   600         \n         1851-06-25 12:00:00              UNNAMED  18510625  1200         \n         1851-06-25 18:00:00              UNNAMED  18510625  1800         \n         1851-06-25 21:00:00              UNNAMED  18510625  2100     L   \n\n                             Status   lat   lon  Maximum Wind  \\\ntraj_id  DateTime                                               \nAL011851 1851-06-25 00:00:00     HU  28.0 -94.8            80   \n         1851-06-25 06:00:00     HU  28.0 -95.4            80   \n         1851-06-25 12:00:00     HU  28.0 -96.0            80   \n         1851-06-25 18:00:00     HU  28.1 -96.5            80   \n         1851-06-25 21:00:00     HU  28.2 -96.8            80   \n\n                              Minimum Pressure  Low Wind NE  ...  Low Wind SW  \\\ntraj_id  DateTime                                            ...                \nAL011851 1851-06-25 00:00:00              -999         -999  ...         -999   \n         1851-06-25 06:00:00              -999         -999  ...         -999   \n         1851-06-25 12:00:00              -999         -999  ...         -999   \n         1851-06-25 18:00:00              -999         -999  ...         -999   \n         1851-06-25 21:00:00              -999         -999  ...         -999   \n\n                              Low Wind NW  Moderate Wind NE  Moderate Wind SE  \\\ntraj_id  DateTime                                                               \nAL011851 1851-06-25 00:00:00         -999              -999              -999   \n         1851-06-25 06:00:00         -999              -999              -999   \n         1851-06-25 12:00:00         -999              -999              -999   \n         1851-06-25 18:00:00         -999              -999              -999   \n         1851-06-25 21:00:00         -999              -999              -999   \n\n                              Moderate Wind SW  Moderate Wind NW  \\\ntraj_id  DateTime                                                  \nAL011851 1851-06-25 00:00:00              -999              -999   \n         1851-06-25 06:00:00              -999              -999   \n         1851-06-25 12:00:00              -999              -999   \n         1851-06-25 18:00:00              -999              -999   \n         1851-06-25 21:00:00              -999              -999   \n\n                              High Wind NE  High Wind SE  High Wind SW  \\\ntraj_id  DateTime                                                        \nAL011851 1851-06-25 00:00:00          -999          -999          -999   \n         1851-06-25 06:00:00          -999          -999          -999   \n         1851-06-25 12:00:00          -999          -999          -999   \n         1851-06-25 18:00:00          -999          -999          -999   \n         1851-06-25 21:00:00          -999          -999          -999   \n\n                              High Wind NW  \ntraj_id  DateTime                           \nAL011851 1851-06-25 00:00:00          -999  \n         1851-06-25 06:00:00          -999  \n         1851-06-25 12:00:00          -999  \n         1851-06-25 18:00:00          -999  \n         1851-06-25 21:00:00          -999  \n\n[5 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>Name</th>\n      <th>Date</th>\n      <th>Time</th>\n      <th>Event</th>\n      <th>Status</th>\n      <th>lat</th>\n      <th>lon</th>\n      <th>Maximum Wind</th>\n      <th>Minimum Pressure</th>\n      <th>Low Wind NE</th>\n      <th>...</th>\n      <th>Low Wind SW</th>\n      <th>Low Wind NW</th>\n      <th>Moderate Wind NE</th>\n      <th>Moderate Wind SE</th>\n      <th>Moderate Wind SW</th>\n      <th>Moderate Wind NW</th>\n      <th>High Wind NE</th>\n      <th>High Wind SE</th>\n      <th>High Wind SW</th>\n      <th>High Wind NW</th>\n    </tr>\n    <tr>\n      <th>traj_id</th>\n      <th>DateTime</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">AL011851</th>\n      <th>1851-06-25 00:00:00</th>\n      <td>UNNAMED</td>\n      <td>18510625</td>\n      <td>0</td>\n      <td></td>\n      <td>HU</td>\n      <td>28.0</td>\n      <td>-94.8</td>\n      <td>80</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>...</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n    </tr>\n    <tr>\n      <th>1851-06-25 06:00:00</th>\n      <td>UNNAMED</td>\n      <td>18510625</td>\n      <td>600</td>\n      <td></td>\n      <td>HU</td>\n      <td>28.0</td>\n      <td>-95.4</td>\n      <td>80</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>...</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n    </tr>\n    <tr>\n      <th>1851-06-25 12:00:00</th>\n      <td>UNNAMED</td>\n      <td>18510625</td>\n      <td>1200</td>\n      <td></td>\n      <td>HU</td>\n      <td>28.0</td>\n      <td>-96.0</td>\n      <td>80</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>...</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n    </tr>\n    <tr>\n      <th>1851-06-25 18:00:00</th>\n      <td>UNNAMED</td>\n      <td>18510625</td>\n      <td>1800</td>\n      <td></td>\n      <td>HU</td>\n      <td>28.1</td>\n      <td>-96.5</td>\n      <td>80</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>...</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n    </tr>\n    <tr>\n      <th>1851-06-25 21:00:00</th>\n      <td>UNNAMED</td>\n      <td>18510625</td>\n      <td>2100</td>\n      <td>L</td>\n      <td>HU</td>\n      <td>28.2</td>\n      <td>-96.8</td>\n      <td>80</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>...</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n      <td>-999</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\"\"\"\n",
    "    1. Reading the atlantic dataset, cleaning it up and then\n",
    "       converting it to PTRAILDataFrame.\n",
    "    2. It is to be noted that apart from reading the dataset,\n",
    "       before converting to PTRAILDataFrame, the dataframe needs\n",
    "       some cleanup as the Time format provided in the dataframe\n",
    "       needs to be first converted into a library supported time\n",
    "       format. Also, the format of the coordinates need to be\n",
    "       converted to library supported format before converting'\n",
    "       it to PTRAILDataFrame.\n",
    "    3. Also, lets, print the first 5 points of the dataset to\n",
    "      see how the dataframe looks.\n",
    "\"\"\"\n",
    "atlantic = pd.read_csv('./data/atlantic.csv')\n",
    "atlantic = con.convert_directions_to_degree_lat_lon(atlantic, 'Latitude',\"Longitude\")\n",
    "def convert_to_datetime(row):\n",
    "        this_date = '{}-{}-{}'.format(str(row['Date'])[0:4], str(row['Date'])[4:6], str(row['Date'])[6:])\n",
    "        this_time = '{:02d}:{:02d}:00'.format(int(row['Time']/100), int(str(row['Time'])[-2:]))\n",
    "        return '{} {}'.format(this_date, this_time)\n",
    "atlantic['DateTime'] = atlantic.apply(convert_to_datetime, axis=1)\n",
    "atlantic_df = PTRAILDataFrame(atlantic,\n",
    "                              latitude='Latitude',\n",
    "                              longitude='Longitude',\n",
    "                              datetime='DateTime',\n",
    "                              traj_id='ID',\n",
    "                              rest_of_columns=[])\n",
    "atlantic_df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True     50\n",
      "False     1\n",
      "Name: DateTime, dtype: int64\n",
      "CPU times: user 19.6 ms, sys: 0 ns, total: 19.6 ms\n",
      "Wall time: 18.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Here, we will filter out a single trajectory from the atlantic\n",
    "# dataset and check how many of the trajectory's points have\n",
    "# time jump greater than what the examples are using to interpolate\n",
    "\n",
    "small_atlantic = atlantic_df.reset_index().loc[atlantic_df.reset_index()[const.TRAJECTORY_ID] == 'AL062010'][[const.TRAJECTORY_ID, const.DateTime, const.LAT, const.LONG]]\n",
    "time_del = small_atlantic.reset_index()[const.DateTime].diff().dt.total_seconds()\n",
    "print((time_del > 3600*4).value_counts())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
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      "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, we plot the smaller trajectory on a folium map.\n",
    "sw = small_atlantic[['lat', 'lon']].min().values.tolist()\n",
    "ne = small_atlantic[['lat', 'lon']].max().values.tolist()\n",
    "coords = [zip(small_atlantic[const.LAT], small_atlantic[const.LONG])]\n",
    "m2 = folium.Map(location=[small_atlantic[const.LAT].iloc[0],\n",
    "                          small_atlantic[const.LONG].iloc[0]],\n",
    "                zoom_start=1000)\n",
    "\n",
    "folium.PolyLine(coords,\n",
    "                color='blue',\n",
    "                weight=2,\n",
    "                opacity=0.7).add_to(m2)\n",
    "m2.fit_bounds([sw, ne])\n",
    "display(m2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 51\n",
      "Interpolated DF Length: 101\n",
      "CPU times: user 10.5 ms, sys: 20.1 ms, total: 30.6 ms\n",
      "Wall time: 279 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using linear interpolation.\n",
    "\n",
    "small_linear_atlantic = ip.interpolate_position(small_atlantic,\n",
    "                                                     time_jump=3600*4,\n",
    "                                                     ip_type='linear')\n",
    "print(f\"Original DF Length: {len(small_atlantic)}\")\n",
    "print(f\"Interpolated DF Length: {len(small_linear_atlantic)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF Length: 51\n",
      "Interpolated DF Length: 101\n",
      "CPU times: user 4.39 ms, sys: 24.2 ms, total: 28.6 ms\n",
      "Wall time: 261 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Now, on the smaller dataframe containing only a single\n",
    "# trajectory from the original dataframe, interpolate the\n",
    "# trajectory using cubic interpolation.\n",
    "\n",
    "small_cubic_atlantic = ip.interpolate_position(small_atlantic,\n",
    "                                                    time_jump=3600*4,\n",
    "                                                    ip_type='cubic')\n",
    "print(f\"Original DF Length: {len(small_atlantic)}\")\n",
    "print(f\"Interpolated DF Length: {len(small_cubic_atlantic)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x1800 with 3 Axes>",
      "image/png": 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QbG4mDx4kDx82z5ubVv4BAEwDASAwdr00qE+Sp55K9vaSum6et7aSxcWk3R5+jQAAADdJp9PcNOkmSgCA6SQABMaulwb1SRP8ParbTQ4OkpUVv8QCAAA8rd1ubpbc2nITJQDAtBIAAmN3XoP6orj49d1ucvfucGoDAAC4STqd5ibJg4Pmd6VHuYkSAGB6CACBa+GsBvWPP37xa7vdZHt7+DUCAABcd3funAz+jnMTJQDA5Cvq43vqjdHS0lK9u7s77jKAa2Rm5uTWn2fNe/hw+PUAAABcZ/PzzXafvcx78GD49QAAMFxFUTxZ1/XS8XErAIFrbW5usPMAAAAm2f7+YOcBAHAzCQCBa2119WRfwONareTWrdHUAwAAcJ25iRIAgEQACFxzGxu9BYC3b4+mHgAAgOvMTZQAACQCQOCaW1hIdnaS2dmTv8S2Ws34zk4zDwAAYNq5iRIAgEQACNwAy8vJ/fvJ2lrTqH5mpnleW2vGl5fHXSEAAMD14CZKAACSpKjretw1PGNpaane3d0ddxkAAAAAN1qnk9y9m2xvJ/v7Tc+/W7ealX/CPwCAyVEUxZN1XS+dGBcAAgAAAAAAwM1zVgBoC1BgqnQ6yfr60a1E19ebcQAAAAAAmAQCQGBqtNvJ4mKytZXs7SV13TxvbTXj7fa4KwQAAAAAgP4JAIGp0OkkKyvJwUHS7R491u024ysrVgICAAAAAHDzCQCBqXDnzsng77huN7l7dzT1AAAAAADAsAgAgalw715vAeD29mjqAQAAAACAYREAAlNhf3+w8wAAAAAA4LoSAAJTYW6u93mdTrK+nszPJzMzzfP6uv6AAAAAAADcDAJAYCqsriat1vlzWq3kkz4pWVxMtraSvb2krpvnra1mvN0eTb0AAAAAAHBVAkBgKmxsXBwAPvZY8v3fnxwcnOwX2O024ysrVgICAAAAAHC9CQCBqbCwkOzsJLOzJ4PAVqsZ/9RPTZ566vzzdLvJ3bvDqxMAAAAAAPolAASmxvJycv9+srZ2tL/f2loz/oM/eHLl33HdbrK9PZp6AQAAAADgKoq6rsddwzOWlpbq3d3dcZcBTKmZmabnXy/zHj4cfj0AAAAAAHCeoiierOt66fi4FYAAh+bmBjsPAABglDqdZH396I4n6+v6mAMATCMBIMCh1dWT/QGPa7WSW7dGUw8AAECv2u1kcTHZ2kr29prdTfb2mq8XF5vjAABMDwEgwKGNjd4CwNu3R1MPAABALzqdZGUlOTg42de8223GV1asBAQAmCYCQIBDCwvJzk4yO3syCGy1mvGdnWYeAADAdXHnzsng77huN7l7dzT1AAAwfgJAgEcsLyf37ydra0f7ZqytNePLy+OuEAAA4Kh793oLALe3R1MPAADjJwAEOGZhIdncTB48SB4+bJ43N3tf+dfpJOvrRwPE9XXb7QAAAMOxvz/YeQAA3HwCQIABareTxcVkayvZ20vqunne2mrG2+1xVwgAAEyaubnBzgMA4OYTAAIMSKeTrKwkBwcnt9/pdpvxlRUrAQEAgMFaXT3Zx/y4Viu5dWs09QAAMH4CQIABuXOnt74bd++Oph4AAGA6bGwkjz9+/pxWK7l9ezT1AAAwfgJAgAG5d6+3AHB7ezT1AAAA0+G//tfk3e8++/iznpXs7PTe1xwAgJtPAAgwIPv7g50HAABwkadbEbzrXWfPKYrkwz98dDUBADB+AkCAAZmb623e7Gyyvp7MzyczM83z+rregAAAwOX10org4UOtCAAApo0AEGBAVlebvhrneeyx5J3vTLa2kr29pK6b562tZHExabdHUysAADAZtCIAAOA0AkCAAdnYuDgAfPgweeqpk7+gd7vJwUGzdY+VgAAAQK+0IgAA4DQCQIABWVhIdnaaLT6PB4GtVrP677HHzj9Ht2trHgAAoHe9tiLodR4AAJNBAAgwQMvLyf37ydra0R5/a2vJs5/drAA8j615AACAy+ilFUGrldy6NZp6AAC4Hoq6rsddwzOWlpbq3d3dcZcBMBQzM03Pv17mXRQUAgAAJE0LgcXFpqXAWWZnmxsVFxZGVxcAAKNRFMWTdV0vHR+3AhBgRGzNAwAADNpFrQhmZ5vjwj8AgOkiAAQYEVvzAAAAw3BeK4L795vjAABMF1uAAoyIrXkAAAAAABgkW4ACjJmteQAAAAAAGAUBIMAIDWJrnk4nWV8/+vr19WYcAAAAAABsAQpwg7TbycpK0u02j6e1Ws1jZ0d/DwAAAACAaWELUIAbrtNpwr+Dg6PhX9J8fXDQHLcSEAAAAABguvUVABZF8WVFUfxiURRvOHx85iPHXlUUxVuKovjpoig+vf9SAabbnTsng7/jut3k7t3R1AMAAAAAwPU0iBWAd+u6funh47uSpCiKj0ryiiQfneQzknxNURSPDeBaAFPr3r3eAsDt7dHUAwAAjJ8e4QAAnGZYW4C+LMlr6rp+V13XP5vkLUk+bkjXApgK+/uDnQcAANxs7XayuJhsbSV7e0ldN89bW814uz3uCgEAGJdBBIB/pSiK+0VRfF1RFO9zOPb8JL/wyJy3Ho6dUBTFWlEUu0VR7L7tbW8bQDkAk2lurrd5s7PuAAYAgEmnRzgAAOe5MAAsiuL7iqJ44ymPlyX5p0kWkrw0yS8nufP0y045VX3a+eu6fnVd10t1XS8997nPvdp3ATAFVleTVuv8OY89lrzzne4ABgCASadHOAAA5ynq+tRc7vInKooXJfnOuq5fUhTFq5Kkruu/d3js3yb5srquf/i8cywtLdW7u7sDqQdg0nQ6TYh3cHD1c8zOJvfvJwsLg6sLAAAYvfn55ma/XuY9eDD8egAAGI+iKJ6s63rp+HhfW4AWRfGBj3z5J5O88fDPr0vyiqIonlUUxYckeXGS1/dzLYBpt7CQ7Ow0Id7xlYCtVrP677HHzj+HO4ABAOBm63SaLf57Cf8SPcIBAKZVvz0A/0FRFD9RFMX9JH80ye0kqev6TUlem+Qnk3x3ki+q6/phn9cCmHrLy80KvrW1oz3+1taSZz87eXjBO223m2xvj6ZWAABgsNrtZleQra3eX9NrL3EAACbLwLYAHQRbgAJc3cxM0/Ovl3kXBYUAAMD1cpWWAK1Wc7Pg5ubw6gIAYLyGsgUoANdHr3f2ugMYAABunjt3mh09LqPVSm7fHk49AABcbwJAgAmxunqyN+BxrVZy69Zo6gEAAAbn3r3eA8BWq+kdvrPT9BIHAGD6CAABJsTGRm8BoDuAAQDg5tnf733u2lrTO3x5eXj1AABwvQkAASbEwkJzh+/s7MkgcBB3AHc6yfp6Mj/f9BGcn2++7nT6rx0AADhfr1v5z883Pf+s/AMAmG4CQIAJsrzc3Om7tnY0qOv3DuB2O1lcTLa2kr29pK6b562tZrzdHuz3AQAAHGXLfwAALqOo63rcNTxjaWmp3t3dHXcZADyi02lCvoODs+fMzjYBo7uMAQBgOPxcDgDAaYqieLKu66Xj41YAAnCuO3eSbvf8Od1ucvfuaOoBAIBpNOwt/wEAmCwCQADOde9ebwHg9vbRMT0DAQCgd738/DysLf8BAJg8tgAF4FwzM03Pv17mPXzY/LndTlZWmmDw0fCw1WoeOzs+nAAAgKf5+RkAgKuyBSgAVzI3d7l5nU7z4cXBwcmVg91uM76yYiUgAAAkfn4GAGA4BIAAnGt19WSPkeNareTWrebPegYCAEDv/PwMAMAw2AIUgHN1OsniYnPn8VlmZ5ueIwsLTQ+Svb2Lzzs/nzx4MLg6AQDgJvLzMwAA/bAFKABXsrDQ9ByZnT25ErDVasZ3dpp5SbK/39t5e50HAACTpNNJ1tebQG9mprfwL/HzMwAAlyMABOBCy8vNCr+1tfd8UDE/33x9/35z/GmX7RkIAADTot1udtfY2mqCv8tsyuTnZwAALkMACEBPFhaSzc1m26GHD5vnzc33rPx72mV7BgIAwDTodJKVlWZr/Yt6/h3n52cAAC5LAAjAQG1s9BYA3r49+Gsf305pfr75utMZ/LUAAOAy7ty5fPD3tGH9/AwAwOQSAAIwUJftGTgop22ntLfXfL242BwHAIBxuXfvaiv/hvXzMwAAk00ACMDAXaZn4CCct51St9uMr6xYCQgAwPjs7/c+d9g/PwMAMPkeH3cBAEymp3sGbm4O/1q9bKfU7SZ3746mHgAAOG5urtmh4iLz802/bQAA6IcVgADceL1sp9TtJtvbZx/XPxAAgGFaXe2tV/atW6OpBwCAySYABODG63U7pbPm6R8IAMCwbWz0FgDevj2aegAAmGwCQABuvLm5q8/TPxAAgKu47A4SCwvJzk4yO3syCGy1mvGdnWYeAAD0SwAIwI3Xz3ZKl+kfCAAAydV3kFheTu7fT9bWjgaHa2vN+PLyaL8PAAAmV1HX9bhreMbS0lK9u7s77jIAuGE6neaDloODs+fMzjYfqhy/o3p+vvmw5iLz88mDB/3VCQDAzdfPz54AADBoRVE8Wdf10vFxKwABuPH62U6p3/6BAABMFztIAABwEwgAAZgIV91OqZ/+gQAA3GyX7eOXJPfu9RYAbm8PtlYAALgMASAAE2NhIdncbLbqfPiwed7cPH/rpX76B/brKh84AQAwGFft42cHCQAAbgI9AAGYauPq4dJuJysrzd3hj95B3mo1j52ds1ctAgDQHz2kAQCYFHoAAsAp+ukfeFWdThP+HRyc3D6q223GV1asBAQAGJZ++viNcwcJAADolQAQgKl31f6BV9XPB04AAPSvnz5+Gxu9BYC3b1+9PgAA6JcAEABytf6BV9XPB05P0z8QAODq+unjN44dJAAA4LIEgAAwYv184JQ0/QMXF5Otrab/TF03z1tbzXi7PbhaAQAm0dxcf/NGvYMEAABclgAQAEasnw+c9A8EACbVKHc4GEQfv1HuIAEAAJclAASAEevnAyf9AwGASTTqHQ708QMAYNIVdV2Pu4ZnLC0t1bu7u+MuAwCGqtNpPsg6ODh7zuxss33U8TvI5+ebD8MuMj/f3IUOAHDd9fOzUT/a7WbnhG736A1WrVbz2NmxlScAANdfURRP1nW9dHzcCkAAGLGFheYDpdnZk3eet1rN+M7O6R9w9ds/EADguhnXDgf6+AEAMMmsAASAMel0mg+ytrebwG5urtn28/bts+9utwIQAJg0fr4BAICrswIQAK6ZhYVkc7P5IOvhw+Z5c/P8ra366R94WZ1Osr5+9I749fVmHADguKv+7GCHAwAAGDwrAAHgBhlVjxw9cQCAy+jnZwcrAAEA4OqsAASACdBP/8BedTrNB3gHByf78XS7zfjKipWAAECj358dRrnDAQAATAsBIADcMMvLzQq/tbWjW2ytrTXj/a7Mu3Pn5Id3x3W7Tf9CAIB+f3bY2OgtALx9+2r1AQDANLIFKABwxFW34ep0mg8A791revTMzTV39G9s9LciEQC43gaxhaftxwEA4GpsAQoA9GR///Lz2u2mN+HWVvMBYF03z1tbzXi7PZxaAYDxu8rPDscNe4cDAACYNlYAAgBHXPYu/k6nCfkODs6eOzvbfHhnJSAATJ5BrAAEAACuxgpAAKAnq6u99eG5dav5s56BADDdLvuzAwAAMHxWAAIAR1x2RZ+7/gFgutkNAAAAxscKQACgJwsLyc5O80Hd8bv5W61mfGfnPR/gDaLvDwDQv04nWV8/2kNvfb0ZH6bL/uwAAAAMnwAQADhhebm5S39t7eiHiGtrzfjy8nvmzs31ds5e5z1qXB9kAsBN0243q/C2tpqV+XXdPG9tNePt9nCvf5mfHQAAgOGzBSgA0Jf19ebDxfP6ALZazQeAm5u9n7fdTlZWmvM+eu5Wq3ns7PgwEQASW3ACAMA0swUoADAUGxsnt/s6rtVKbt/u/ZydThP+HRycDBa73WZ8ZcVKQABIkjt3zr8RJ2mO3707mnoAAIDxEwACAH0ZRt+fy36QaatQAK6bUf7bdO9eb/9ubm8P/toAAMD1ZAtQAGAgOp0mkNveTvb3m55/t241K/8uu93Y/HzTt6iXea95ja1CAbheRr2N9cxM0/Ovl3kPHw7uugAAwPidtQWoABAAuHZ6/SCzKJInntDzCIDrYxz9+C5z48yDB4O5JgAAcD3oAQgA3Bhzc73Ne/xxPY8AuF7G0Y9vdbW3fry3bg3umgAAwPUmAAQArp1eP8hM9DwC4HoZRz++jY3e/t28fXtw1wQAAK43ASAAcO30+kHmU0/1dr79/f5rAoBe9PpvziD/bVpYaPoKzs6e/Pez1WrGd3Zshw0AANNEAAgAXDu9fpDZ61ahvc4DgH6N69+m5eWmr+DaWtPrb2ameV5ba8aXlwd7PQAA4HoTAAIA11IvH2TqeQTAdTPOf5sWFpLNzeTBg+Thw+Z5c9PKPwAAmEZFXdfjruEZS0tL9e7u7rjLAABuiE4nWVxMDg7OnjM72wSGvXz42ekkd+40/Zv295vVGaurzZakPjwFoBeD/rcJAADgPEVRPFnX9dLxcSsAAYAba5A9j9rt5gPbra1kby+p6+Z5a6sZb7eH8z0ATJNOJ1lfP7qye329GZ8U+vEBAADXgQAQALjRBtHzqNNJVlaa1Rrd7tFj3W4zvrIyWR9QA4zaNN1ooR8fAAAwbrYABQCm3vp68wH08fDvUa1W88Ht7du2CQW4LNtiAgAADIctQAEAznDv3vnhX9Ic/7qvm57VKwCDdOdOb++zd++Oph4AAIBJZwUgADD1ZmaaMK9fVq8AnG5+vrlhopd5Dx4Mvx4AAIBJYQUgAMAZ5uYGcx6rVwBOt78/2HkAAACcTwAIAEy91dWmx1+/ut1ke7v/8wBMml5vtBjUDRkAAADTTgAIAEy9jY3BBICJ1SvAzdTpJOvrzRacMzPN8/p6Mz4Ivdxo0Wolt24N5noAAADTTgAIAEy9hYVkZ6fp4Xf8A+pWqxl/4onezmX1CnDTtNvJ4mKytdX06avr5nlrqxlvt/u/Ri83WrRaye3b/V8LAAAAASAAQJJkeTm5fz9ZWzu6AmZtrRn/gi8Y7OqVYa+2AehFp5OsrCQHB802xo/qdpvxlZX+35t6udFiZ6eZBwAAQP+Kuq7HXcMzlpaW6t3d3XGXAQBwQqfTrIQ5ODh7zuxsExZe9AF2u918oN7tHv3AvdVqHjs7TSAJMGzr681Kv+Ph36NareZmiM3N/q/X6SR37zb9Uvf3m1XTt241K/+EfwAAAJdXFMWTdV0vnRgXAAIA9GYQwd0gg0SAfs3PN9t99jLvwYPh1wMAAMDlnBUA2gIUAKBHF20T2suqvTt3zl9pkzTH794dTM0A59nfH+w8AAAArgcrAAEARugyq21+7MeawPDevfdslbe6mmxsWB0IDIYVgAAAADebFYAAANdAr6to9vaarUK3tpo/13XzvLXVjLfbw60TmA6rq80WxudptZo+fQAAANwcAkAAgBGam+ttXl03fQKPbxfa7TbjKytNP0GAfmxs9BYA3r49mnoAAAAYDAEgAMAI9bLapiiax3n0CQQGYWEh2dlJZmdPvje1Ws34zo5thwEAAG4aASAAwAj1stqmrpvHebrdZHt7cHUB02t5Obl/P1lba3r9zcw0z2trzfjy8rgrBAAA4LKK+qJPl0ZoaWmp3t3dHXcZAABD1W43W3h2u0e3+Gy1msc73nFxAJg0H9I/fDi8OgEAAAC43oqieLKu66Xj41YAAgCM2EWrbXrtE9jrPGDwOp1kff3o3+H1db05AQAAuB4EgAAAY7CwkGxuJg8eNKv4Hjxovl5Y6K1PYKuV3Lp1+jHBBAxXu50sLiZbW8neXrNid2+v+XpxsTkOAAAA42QLUACAa6bTaUKEg4Oz58zONqsFFxaOjl+0vejOjn5e0I9+/n4CAADAoNkCFADghlhYaIK62dmTKwFbrWZ8Z+dkuNDpNOHfwcHR8C9pvj44aI5bCQhXd+fOyb9fx3W7yd27o6kHAAAATiMABAC4hi7qE3jaKj7BBAzfvXu9/T3b3h5NPQAAAHAaASAAwDV1Xp/A0/QaTLz61foDMt366ZO5v9/bNXqdBwAAAMMgAAQAmBC9Bg7dbrK3l9R187y11fQ0a7eHWx9cB+1289/71tbV/h7MzfV2nV7nAQAAwDAIAAEAJsRVAwf9AZkWg+iTubp6sjfnca1WcutW//UCAADAVQkAAQAmRC/BxHn0B2TSDaJP5sZGbwHg7duXrw8AAAAGRQAIADAhegkmztPtJtvbg6sHrpte+2Se9/dgYSHZ2UlmZ0/+fWu1mvGdnbN7dQIAAMAoCAABACbEecFEr3rtIwg3Ua//fV80b3k5uX8/WVtL5ueTmZnmeW2tGV9e7r9WAAAA6EdR1/W4a3jG0tJSvbu7O+4yAAButE6n2cJwe7sJMubmkne84+KVT0kTYjx4MPwaYRzm55O9vd7m+XsAAADATVAUxZN1XS8dH7cCEABgwiwsJJubTYDx8GHz/IVfePGqwMcfT174wqMrmtbXm0ARJkEvfTJbreTWrdHUAwAAAMMiAAQAmAK99Ad86qnkp36qWSFV183z1layuJi026OpE4apl78HrVZy+/Zo6gEAAIBhEQACAEyB8/oDPv74e/781FNHj3W7ycFBsrJiJSA333l/D1qtZnxnp5kHAAAAN5kAEABgSiwvJ/fvJ2trR7f5/IiPOBoCnqbbbfoKQj86nWZb2XFuM3vW34O1tWZ8eXl0tQAAAMCwFHVdj7uGZywtLdW7u7vjLgMAYKrMzzfbfV6k1Ure/Garo7iadrtZSdrtNo+ntVrNY2dH+AYAAACXVRTFk3VdLx0ftwIQAGDK7e/3Nq/b1Q+Qq+l0mvDv4OBo+JfYZhYAAACGQQAIADDl5uZ6nyuo4Sru3DkZ/B1nm1kAAAAYHAEgAMCUW11ttmDslaBmul2lj9+9e70FgNvbg60VAAAAppUegAAAU67Tabb2PDjo/TXz88mDB8Orievpqn38ZmaSXn7tmJlJHj4cXL0AAAAw6fQABADgVAsLTXAzO9v7a3rtG8jk6KePX6/bzF5mO1oAAADgbAJAAACyvJzcv9/7VqBnBTVX2R6Sm6GfPn69bDPbaiW3bl29PgAAAOA9BIAAACRpVgJ+4RdePahpt5utRLe2kr29ZsvHvb3m68XF5jg3Vz99/DY2evvv6vbtq9cHAAAAvIcAEACAZ1w1qOlne0huhl63fT1t3qPbzB7/76vVasZ3dpp5AAAAQP8EgAAAPOOqQU0v20O+4x3JR36krUFvqn77+D29zeza2tEtYtfWmvHl5cHVCgAAANOu7wCwKIovLorip4uieFNRFP/gkfFXFUXxlsNjn97vdQAAGI2rBDW9bA9Z180cW4MOxqj7LQ6ij9/CQrK5mTx4kDx82Dxvblr5BwAAAINW1HV99RcXxR9N8qVJ/kRd1+8qiuJ313X9a0VRfFSSb07ycUmel+T7knx4XdcPzzvf0tJSvbu7e+V6AAAYj5mZJti7itnZJlgUAvWu3W62VO12jwavrVbz2NkZ/Iq6TqcJbA8Ozp7j/0sAAAAYraIonqzreun4eL8rAP9ykr9f1/W7kqSu6187HH9ZktfUdf2uuq5/Nslb0oSBAABMoF63hzxNt5vcvXv6sVGvcrtuTvv+X/nK5OUvH32/RX38AAAA4OboNwD88CSfWBTFfy6K4geKovjYw/HnJ/mFR+a99XDshKIo1oqi2C2KYvdtb3tbn+UAADAOvWwPeZZuN9nePjnebjcrzra2mi1Dp23r0LO+/2/+5qaf4nnOC1X7oY8fAAAA3AwXbgFaFMX3JfmAUw59aZKvTPLvkvy1JB+b5FuSfGiSzSQ/XNf1vcNzfG2S76rr+l+ddy1bgAIA3Ey9bA95npmZpifcZc737Gc3K+Fe97pkf79Zhbi6mmxs3PxVaP3+75k0wdyDB4OrCQAAALh+rrwFaF3Xn1bX9UtOefzrNCv7vrVuvD7Ju5O83+H4Bz1ymhck+aXBfCsAAFw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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, plot the scatter points of following 3 trajectories:\n",
    "# 1. Original Small DF.\n",
    "# 2. Linear-Interpolated Small DF.\n",
    "# 3. Cubic-Interpolated Small DF.\n",
    "\n",
    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(25, 25))\n",
    "axes[0].scatter(small_atlantic[const.LAT],\n",
    "                small_atlantic[const.LONG],\n",
    "                s=100, color='brown')\n",
    "axes[0].set_title('Original', fontsize=60, color='grey')\n",
    "axes[1].scatter(small_linear_atlantic[const.LAT],\n",
    "                small_linear_atlantic[const.LONG],\n",
    "                s=100, color='blue')\n",
    "axes[1].set_title('Linear', fontsize=60, color='grey')\n",
    "axes[2].scatter(small_cubic_atlantic[const.LAT],\n",
    "                small_cubic_atlantic[const.LONG],\n",
    "                s=100, color='green')\n",
    "axes[2].set_title('Cubic', fontsize=60, color='grey')\n",
    "\n",
    "fig.tight_layout()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF length: 89869\n",
      "Linear Interpolated DF length: 157775\n",
      "CPU times: user 266 ms, sys: 145 ms, total: 411 ms\n",
      "Wall time: 40.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\"\"\"\n",
    "    Finally, here we show how an entire Dataframe containing\n",
    "    several trajectories can be passed to the interpolation\n",
    "    function and how the number of points will be added\n",
    "    to the dataframe based on the user-provided time jump.\n",
    "\"\"\"\n",
    "\n",
    "# Here, Interpolate the original seagulls dataset using linear\n",
    "# interpolation and then show the difference in the number of\n",
    "# points to see how the dataframe's trajectories have been\n",
    "# interpolated.\n",
    "\n",
    "linear_ip_gulls = ip.interpolate_position(dataframe=gulls_df,\n",
    "                                 time_jump=3600*4)\n",
    "print(f\"Original DF length: {len(gulls_df)}\")\n",
    "print(f\"Linear Interpolated DF length: {len(linear_ip_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original DF length: 89869\n",
      "Cubic Interpolated DF length: 157775\n",
      "CPU times: user 177 ms, sys: 117 ms, total: 294 ms\n",
      "Wall time: 40.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Here, Interpolate the original seagulls dataset using cubic\n",
    "# interpolation and then show the difference in the number of\n",
    "# points to see how the dataframe's trajectories have been\n",
    "# interpolated.\n",
    "\n",
    "cubic_ip_gulls = ip.interpolate_position(dataframe=gulls_df,\n",
    "                                   time_jump=3600*4,\n",
    "                                   ip_type='cubic')\n",
    "print(f\"Original DF length: {len(gulls_df)}\")\n",
    "print(f\"Cubic Interpolated DF length: {len(cubic_ip_gulls)}\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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